IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/ .

IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.

PFAS Exposures and the Human Metabolome: A Systematic Review of Epidemiological Studies

Purpose of Review

There is a growing interest in understanding the health effects of exposure to per- and polyfluoroalkyl substances (PFAS) through the study of the human metabolome. In this systematic review, we aimed to identify consistent findings between PFAS and metabolomic signatures. We conducted a search matching specific keywords that was independently reviewed by two authors on two databases (EMBASE and PubMed) from their inception through July 19, 2022 following PRISMA guidelines.

Recent Findings

We identified a total of 28 eligible observational studies that evaluated the associations between 31 different PFAS exposures and metabolomics in humans. The most common exposure evaluated was legacy long-chain PFAS. Population sample sizes ranged from 40 to 1,105 participants at different stages across the lifespan. A total of 19 studies used a non-targeted metabolomics approach, 7 used targeted approaches, and 2 included both. The majority of studies were cross-sectional (n = 25), including four with prospective analyses of PFAS measured prior to metabolomics.

Summary

Most frequently reported associations across studies were observed between PFAS and amino acids, fatty acids, glycerophospholipids, glycerolipids, phosphosphingolipids, bile acids, ceramides, purines, and acylcarnitines. Corresponding metabolic pathways were also altered, including lipid, amino acid, carbohydrate, nucleotide, energy metabolism, glycan biosynthesis and metabolism, and metabolism of cofactors and vitamins. We found consistent evidence across studies indicating PFAS-induced alterations in lipid and amino acid metabolites, which may be involved in energy and cell membrane disruption.

Maternal Microbial Metabolites and Risk of Fetal Growth Extremes: A Longitudinal Multi-Racial/Ethnic Cohort Study

Objectives

Fetal growth extremes [small- and large-for-gestational age (SGA and LGA)] represent high-risk phenotypes for cardiometabolic disorders. Metabolomic profiling of the in-utero milieu may elucidate pathophysiology of fetal growth extremes and inform dietary recommendations and upstream risk factors. We aimed to identify microbiome-derived metabolites associated with SGA and LGA.

Methods

A random sample of 140 SGA, 134 LGA and 140 appropriate for gestational age (AGA) was drawn from the Pregnancy Environment and Lifestyle Study cohort. Using fasting serum at gestational weeks (GW) 10–13 and 16–19,1167 known metabolites were measured by gas and liquid chromatography (LC)/time-of-flight mass spectrometry (TOF-MS) and hydrophilic interaction LC/quadrupole TOF-MS, of which 165 microbial metabolites were linked with the Virtual Metabolic Human Database. After adjusting for sociodemographic, lifestyle, reproductive history, and clinical factors, we identified significant pathways associated with risk of SGA and LGA vs. AGA using chemical similarity enrichment analysis, controlling for the false discovery rate (P_FDR).

Results

At GW 10–13, branched-chain amino acids (AA), dicarboxylic acids (DA) and medium-chain hydroxy acids were positively, while phosphatidylcholines, saturated fatty acids (FA) and glucogenic AA were inversely associated with SGA risk (all P_FDR < 0.01). At GW 16–19, positive associations of branched-chain AA and DA and inverse associations of saturated FA with SGA risk persisted, while unsaturated triglycerides (TG) were inversely associated with SGA risk (all P_FDR < 0.01). At GW 10–13, glucogenic AA, DA, hippurates, phenylacetylglutamine and cresols were positively, whereas phosphatidylcholine and unsaturated TG were inversely associated with LGA risk (all P_FDR < 0.04). At GW 16–19, carnitine, saturated TG, cyclic AA, DA, glyceric acids, phenylacetylglutamine and cresols were positively, while aromatic, basic and sulfur AA, sugar alcohols, phosphatidylcholines and unsaturated TG were inversely associated with LGA risk (all P_FDR < 0.05).

Conclusions

Distinct microbial metabolites in early to mid-pregnancy are associated with SGA and LGA risk, calling for further investigation into microbiome-metabolome-host interactions.

Funding Sources

The research was supported by NIDDK, NIEHS, and NIH Office of Directors.

Comparison of untargeted and targeted perfluoroalkyl acids measured in adolescent girls

Quantitative biomonitoring (e.g., targeted analysis) has served as the gold standard for environmental exposure biomonitoring for several decades. Recent advancements to broaden exposomic research brought new semi-quantitative untargeted assays that capture a wide range of endogenous metabolites and exogenous exposures in a single assay for discovery, though usually at the expense of absolute quantitation. The high-resolution mass spectrometers (HRMS) typically used in untargeted workflows are sensitive and robust, but there do not yet exist comprehensive comparisons between environmental chemicals at population exposure levels measured using targeted and untargeted assays. Using liquid chromatography (LC)-HRMS, we measured per- and polyfluoroalkyl substances (PFAS) including perfluorohexane sulfonate (PFHxS), n-perfluorooctanoic acid (PFOA), n-perfluorooctanesulfonic acid (PFOS), and perfluorononanoic acid (PFNA) in plasma of 180 girls from New York City, and compared them to previously obtained targeted measures using correlation and rank order methods. We showed high agreement between the methods with Spearman Rhos ranging from 0.69 to 0.92 and weighted Kappa's from 0.62 to 0.82 for tertiles among the PFAS. This finding demonstrates that semi-quantitative data from untargeted assays designed for exposomics can be reliably used to estimate environmental exposures occurring in the general population, providing an economic alternative to targeted assays. We also describe an approach that can be used to compare relative quantitation measurements from an untargeted assay to traditional targeted measures to establish fit-for-purpose usability and validation. These results suggest that environmental exposure measures from untargeted assays can serve as reliable inputs into statistical analysis for discovery and for determining their resultant biological impacts. Future efforts to develop new statistical approaches for standardization and merging with targeted measures-toward harmonization-will further enhance the utility of untargeted assays in environmental epidemiology.

An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry

Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.

Functional Microbiomics Reveals Alterations of the Gut Microbiome and Host Co-Metabolism in Patients With Alcoholic Hepatitis

Alcohol‐related liver disease is a major public health burden, and the gut microbiota is an important contributor to disease pathogenesis. The aim of the present study is to characterize functional alterations of the gut microbiota and test their performance for short‐term mortality prediction in patients with alcoholic hepatitis. We integrated shotgun metagenomics with untargeted metabolomics to investigate functional alterations of the gut microbiota and host co‐metabolism in a multicenter cohort of patients with alcoholic hepatitis. Profound changes were found in the gut microbial composition, functional metagenome, serum, and fecal metabolomes in patients with alcoholic hepatitis compared with nonalcoholic controls. We demonstrate that in comparison with single omics alone, the performance to predict 30‐day mortality was improved when combining microbial pathways with respective serum metabolites in patients with alcoholic hepatitis. The area under the receiver operating curve was higher than 0.85 for the tryptophan, isoleucine, and methionine pathways as predictors for 30‐day mortality, but achieved 0.989 for using the urea cycle pathway in combination with serum urea, with a bias‐corrected prediction error of 0.083 when using leave‐one‐out cross validation. Conclusion: Our study reveals changes in key microbial metabolic pathways associated with disease severity that predict short‐term mortality in our cohort of patients with alcoholic hepatitis.

Perspective: Dietary Biomarkers of Intake and Exposure-Exploration with Omics Approaches

While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research.

Abstract B12: Analysis of Lipid Metabolism and Gene Expression in Hepatocellular Carcinoma Reveals Mitochondrial Function as a Potential Target for Combinatorial Treatment

Background: Hepatocellular carcinoma (HCC), the most common malignant liver tumor, is a heterogeneous disease with little efficacious chemotherapy. Lipid metabolic dysfunction is integral to the pathogenesis of HCC, but the underlying mechanisms remain poorly understood. HCC cell lines and patient tumors can be classified into hepatocyte and hepatoblast subtypes based on gene expression patterns, with the hepatoblast type being more aggressive.

Methods: We characterized the RNA expression profiles of 20 HCC cell lines (13 hepatoblast and 7 hepatocyte type) and 1 normal immortalized liver cell line. We also characterized the steady state concentrations of hundred of metabolites of these cells lines. In addition, to find the specific small molecules to inhibit their growth we tested a panel of metabolic pathway inhibitors for activity against these cell lines.

Results: There are significant differences between the transcriptional profiles of the two subtypes of HCC, as well as between HCC cell lines and the normal liver cell line. The most important determinants of these differences are in lipid metabolic pathways. Analysis of the microarray data, corroborated by real time quantitative PCR analysis showed significant difference between transcript levels of ACOX1, ACOX2, FASN, SCD1, and SCAP, as well as in fatty acid transport proteins (FATPs, SLC27As) and elongation enzymes (ELOVL). Metabolomic studies show that not only are there differences between the steady state amounts of long chain and very long chain fatty acids among the groups, but there are also differences in the levels of acyl-carnitines. These differences indicate that there is variability in their reliance on mitochondrial oxidative respiration. Accordingly, there are significant differences between the sensitivity of the HCC cell lines to oligomycin compared with a normal liver cell line.

Conclusion: These data point to the importance of lipid metabolic and mitochondrial oxidative dysfunction in the pathogenesis of hepatocellular carcinoma. We anticipate combining the inhibition of oxidative phosphorylation with the standard of care treatment consisting of sorafenib to allow for a much lower and better tolerated dose of both drugs while maintaining anti-tumor efficacy.

Citation Format: Ali Zarrinpar, Tiziana Palumbo, Dinesh Barupal. Analysis of Lipid Metabolism and Gene Expression in Hepatocellular Carcinoma Reveals Mitochondrial Function as a Potential Target for Combinatorial Treatment [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B12.

Dietary flavonoid intake and colorectal cancer risk in the European prospective investigation into cancer and nutrition (EPIC) cohort

Flavonoids have been shown to inhibit colon cancer cell proliferation in vitro and protect against colorectal carcinogenesis in animal models. However, epidemiological evidence on the potential role of flavonoid intake in colorectal cancer (CRC) development remains sparse and inconsistent. We evaluated the association between dietary intakes of total flavonoids and their subclasses and risk of development of CRC, within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. A cohort of 477,312 adult men and women were recruited in 10 European countries. At baseline, dietary intakes of total flavonoids and individual subclasses were estimated using centre-specific validated dietary questionnaires and composition data from the Phenol-Explorer database. During an average of 11 years of follow-up, 4,517 new cases of primary CRC were identified, of which 2,869 were colon (proximal = 1,298 and distal = 1,266) and 1,648 rectal tumours. No association was found between total flavonoid intake and the risk of overall CRC (HR for comparison of extreme quintiles 1.05, 95% CI 0.93-1.18; p-trend = 0.58) or any CRC subtype. No association was also observed with any intake of individual flavonoid subclasses. Similar results were observed for flavonoid intake expressed as glycosides or aglycone equivalents. Intake of total flavonoids and flavonoid subclasses, as estimated from dietary questionnaires, did not show any association with risk of CRC development.